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46 lines
1.8 KiB
Python
46 lines
1.8 KiB
Python
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import processing
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import pandas as pd
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import gpt_alignment
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# Specify the output file path
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file_path = "alignments_with_training_headlines2.wa"
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# paths to students andsewrs database
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#chunked_path1 = "test_goldStandard/student/STSint.testinput.answers-students.sent1.chunk.txt"
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#chunked_path2 = "test_goldStandard/student/STSint.testinput.answers-students.sent2.chunk.txt"
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#alignment_path = "test_goldStandard/student/STSint.testinput.answers-students.wa"
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# paths to headlines
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chunked_path1 = "test_goldStandard/headlines/STSint.testinput.headlines.sent1.chunk.txt"
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chunked_path2 = "test_goldStandard/headlines/STSint.testinput.headlines.sent1.chunk.txt"
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alignment_path = "test_goldStandard/headlines/STSint.testinput.headlines.wa"
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# load data
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goldstandard_chunked = processing.load_chunked(chunked_path1, chunked_path2)
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goldstandard_alignment = processing.load_alignment(alignment_path)
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# get a nice anwser-student table
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data = pd.merge(goldstandard_chunked, goldstandard_alignment, left_index=True, right_index=True)
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#print(data)
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train, test = processing.generate_train_test_split(data)
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data_for_chat, indexes = processing.get_chunks_as_text(test)
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_, indexes_of_training = processing.get_chunks_as_text(train)
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indexes_of_training = [i+1 for i in indexes_of_training]
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indexes = [i+1 for i in indexes]
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print(indexes_of_training)
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print(indexes)
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client = gpt_alignment.createGPT()
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responses = []
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for i in range(0, 10):#len(data_for_chat)):
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responses.append([gpt_alignment.callApi_examples(client, train, data_for_chat[i]), indexes[i]])
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with open(file_path, 'w') as file:
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for i, r in enumerate(responses):
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file.write("<sentence id=\"" + str(r[1]+1) + "\" status=\"\">\n")
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file.write("<alignment>\n")
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file.write(r[0])
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file.write("\n</alignment>\n")
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file.write("</sentence>\n\n")
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